Multi-dimensional piano performance technique assessment and personalized teaching strategy generation based on deep learning

Automated assessment of piano performance technique, together with the delivery of personalized pedagogical feedback, remains a stubborn problem in intelligent music education. In this work we develop an integrated system that couples multi-dimensional technique evaluation with adaptive teaching-path planning inside a unified multi-task learning framework. Three design choices motivate the novelty here: a multi-granularity Constant-Q Transform feature encoder paired with cross-dimension channel attention; a four-layer Transformer that supplies a shared temporal representation to five dimension-specific projection heads — each scoring pitch accuracy, rhythmic stability, dynamics control, coherence, or expressiveness; and a reinforcement-learning curriculum sequencer that consumes the assessment vector together with a gated knowledge-tracing profile and then produces individualized exercise sequences. On our newly constructed PianoEval dataset of 1,012 expert-annotated performances, the model attains Pearson correlations of 0.91, 0.89, 0.85, 0.80 and 0.74 on the five dimensions respectively (averaging 0.838), with mean squared error of 0.024 and three-tier classification accuracy of 81.7%, beating five baselines on every dimension. In an eight-week controlled study with 45 participants, the full system delivered an absolute mean technique gain of 0.156, compared with 0.112 for assessment-only feedback and 0.086 for teacher-led instruction. The relative gain over traditional instruction therefore reached 81.4% (Cohen’s d = 1.12, 95% CI [0.40, 1.84]), and improvements were statistically significant on every dimension. We read these findings as evidence that tightly coupling fine-grained assessment with closed-loop personalized instruction can yield measurable pedagogical value.